Meteorological predictors of dengue hemorrhagic fever incidence in Cilacap Regency, Indonesia: A time series analysis

Abstract

Objective: To analyze the nonlinear relationship between meteorological predictors and dengue hemorrhagic fever, and to quantify the meteorologically attributable fraction of dengue hemorrhagic fever (DHF) during 2021-2025 in Cilacap Regency.

Methods: This ecological time series design used sequential three-stage analytical tools, including a cross-correlation function (CCF) analysis applied to identify lag structures, Granger causality within a vector autoregression (VAR) framework used to confirm directional temporal precedence, and a quasi-Poisson Distributed Lag Nonlinear Model (DLNM) with natural cubic spline crossbases over a 28-day lag window used to estimate the exposure-response relationship. Autoregressive terms alongside long-term trends and seasonal splines were incorporated to account for autocorrelation and potential confounding.

Results: Granger causality testing confirmed unidirectional of DHF relationships across all four predictors, including mean temperature, rainfall, relative humidity, and wind speed (all P<0.05). Among these, daily rainfall emerged as the dominant predictor [relative risk (RR) 4.23; 95% CI 2.10-8.52)], a peak CCF lag of 7 days, and an attributable fraction of 27.58%. The mean temperature displayed a J-shaped exposure-response curve, with a protective nadir at 26.5°C, and markedly elevated risk above 28 °C [RR 2.13; 95% CI 1.43-3.19; Attributable Fraction (AF) = 6.22%]. Relative humidity (AF= -14.40%) and wind speed (AF= -3.37%) demonstrated complex nonlinear patterns contributing net protective attributable fractions. Overall, the model explained 52.26% of the null deviance (QAIC= 3 984.23).

Conclusions: Rainfall and temperature were the meteorological predictors most strongly associated with DHF incidence in Cilacap Regency. This finding provides evidence for actionable vector control program and early warning system of DHF to consider the utilization of meteorological predictor to prevent DHF outbreak in Cilacap.

Keywords

Tropical disease / Vector-borne disease / Distributed Lag Nonlinear Model / Rainfall / Temperature / Early warning system

References

[1] World Health Organization. Global dengue surveillance. 2025. [Online]. Available from: https://www.who.int/news-room/fact-sheets/detail/dengue-and-severe-dengue. [Accessed on 17 April 2026].

[2] Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. Nature 2013; 496(7446): 504-507.

[3] Colón-González FJ, Gibb R, Khan K, Watts A, Lowe R, Brady OJ. Projecting the future incidence and burden of dengue in Southeast Asia. Nat Commun 2023; 14(1): 5439.

[4] Harapan H, Michie A, Mudatsir M, Sasmono RT, Imrie A. Epidemiology of dengue hemorrhagic fever in Indonesia: Analysis of five decades data from the National Disease Surveillance. BMC Res Notes 2019; 12(1): 350.

[5] Mordecai EA, Caldwell JM, Grossman MK, Lippi CA, Johnson LR, Neira M, et al. Thermal biology of mosquito-borne disease. Ecol Lett 2019; 22(10): 1690-1708.

[6] Chanprasopchai P, Pongsumpun P, Tang IM. Effect of rainfall for the dynamical transmission model of the dengue disease in Thailand. Comput Math Methods Med 2017; 2017: 1-17.

[7] Couper LI, Farner JE, Caldwell JM, Childs ML, Harris MJ, Kirk DG, et al. How will mosquitoes adapt to climate warming? Elife 2021; 10: e69630. doi: https://doi.org/10.7554/eLife.69630.

[8] Gasparrini A. Modeling exposure–lag–response associations with distributed lag non-linear models. Stat Med 2014; 33(5): 881-899.

[9] Lowe R, Gasparrini A, van Meerbeeck CJ, Lippi CA, Mahon R, Trotman AR, et al. Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study. PLoS Med 2018; 15(7): e1002613.

[10] Alam KE, Ahmed MJ, Chalise R, Rahman MA, Mathin TT, Bhuiyan MIH, et al. Time series analysis of dengue incidence and its association with meteorological risk factors in Bangladesh. PLoS One 2025; 20(8): e0323238.

[11] Gudziunaite S, Shabani Z, Weitensfelder L, Moshammer H. Time series analysis in environmental epidemiology: Challenges and considerations. Int J Occup Med Environ Health 2023; 36(6): 704-716.

[12] Wen Q, Gao J, Song X, Sun L, Xu H, Zhu S. RobustSTL: A robust seasonal-trend decomposition algorithm for long time series. Proc AAAI Conf Artif Intell 2019; 33(01): 5409-5416.

[13] Badan Pusat Statistik. Kabupaten Cilacap dalam angka 2026. Kabupaten Cilacap: Badan Pusat Statistik Cilacap; 2026.

[14] Haldar S, Choudhury M, Choudhury S, Samanta P. Trend analysis of long-term meteorological data of a growing metropolitan city in the era of global climate change. Total Environ Res Themes 2023; 7: 100056.

[15] AlSalehy AS, Bailey M. Improving time series data quality: Identifying outliers and handling missing values in a multilocation gas and weather dataset. Smart Cities 2025; 8(3): 82.

[16] Alejo-Sanchez LE, Márquez-Grajales A, Salas-Martínez F, Franco-Arcega A, López-Morales V, Acevedo-Sandoval OA, et al. Missing data imputation of climate time series: A review. MethodsX 2025; 15: 103455.

[17] Cox NJ. Speaking stata: Replacing missing values: The easiest problems. Stata J 2023; 23(3): 884-896.

[18] Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y. Testing the null hypothesis of stationarity against the alternative of a unit root. J Econom 1992; 54(1-3): 159-178.

[19] Pfaff B VAR. SVAR and SVEC models: Implementation within R package vars. J Stat Softw 2008;27(4). doi: 10.18637/jss.v027.i04.

[20] Johansson MA, Cummings DAT, Glass GE. Multiyear climate variability and dengue—El Niño-Southern Oscillation, weather, and dengue incidence in Puerto Rico, Mexico, and Thailand: A longitudinal data analysis. PLoS Med 2009; 6(11): e1000168.

[21] Gasparrini A. Distributed Lag Linear and Non-Linear Models in R: The package dlnm. J Stat Softw 2011;43(8). doi: 10.18637/jss.v043.i08.

[22] Gasparrini A, Scheipl F, Armstrong B, Kenward MG. A Penalized Framework for Distributed Lag Non-Linear Models. Biometrics 2017; 73(3): 938-948.

[23] Couret J, Dotson E, Benedict MQ. Temperature, larval diet, and density effects on development rate and survival of Aedes aegypti (Diptera: Culicidae). PLoS One 2014; 9(2): e87468.

[24] Tjaden NB, Thomas SM, Fischer D, Beierkuhnlein C. Extrinsic incubation period of dengue: Knowledge, backlog, and applications of temperature dependence. PLoS Negl Trop Dis 2013; 7(6): e2207.

[25] Volz EM, Miller JC, Galvani A, Ancel Meyers L. Effects of heterogeneous and clustered contact patterns on infectious disease dynamics. PLoS Comput Biol 2011; 7(6): e1002042.

[26] Mordecai EA, Cohen JM, Evans MV, Gudapati P, Johnson LR, Lippi CA, et al. Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models. PLoS Negl Trop Dis 2017; 11(4): e0005568.

[27] Nik Abdull Halim NMH, Che Dom N, Dapari R, Salim H, Precha N. A systematic review and meta-analysis of the effects of temperature on the development and survival of the Aedes mosquito. Front Public Health 2022;10. doi: 10.3389/fpubh.2022.1074028.

[28] Lambrechts L, Paaijmans KP, Fansiri T, Carrington LB, Kramer LD, Thomas MB, et al. Impact of daily temperature fluctuations on dengue virus transmission by Aedes aegypti. Proc Natl Acad Sci U S A 2011; 108(18): 7460-7465.

[29] Polrob W, La-up A. Nonlinear and lagged effects of climate variability on dengue incidence in an urban megacity: A distributed lag non-linear model (DLNM) based study in Bangkok, Thailand. BMC Public Health 2025; 25(1): 4024.

[30] Sarwar M, Rasool B. Seasonal prevalence and phenomenal biology as tools for dengue mosquito Aedes aegypti (Linnaeus) (Diptera: Culicidae) management. Braz Arch Biol Technol 2022;65. doi: 10.1590/1678-4324-2022220050.

[31] Dalpadado R, Amarasinghe D, Gunathilaka N, Ariyarathna N. Bionomic aspects of dengue vectors Aedes aegypti and Aedes albopictus at domestic settings in urban, suburban and rural areas in Gampaha District, Western Province of Sri Lanka. Parasit Vectors 2022; 15(1): 148.

[32] Wibawa BSS, Wang YC, Andhikaputra G, Lin YK, Hsieh LHC, Tsai KH. The impact of climate variability on dengue fever risk in central java, Indonesia. Clim Serv 2024; 33: 100433.

[33] World Health Organization. Global strategy for dengue prevention and control 2012-2020. Geneva: World Health Organization; 2012.

[34] Nuraini N, Fauzi IS, Fakhruddin M, Sopaheluwakan A, Soewono E. Climate-based dengue model in Semarang, Indonesia: Predictions and descriptive analysis. Infect Dis Model 2021; 6: 598-611.

[35] Méndez-Lázaro P, Muller-Karger F, Otis D, McCarthy M, Peña-Orellana M. Assessing climate variability effects on dengue incidence in San Juan, Puerto Rico. Int J Environ Res Public Health 2014; 11(9): 9409-9428.

[36] Li C, Wang Z, Yan Y, Qu Y, Hou L, Li Y, et al. Association between hydrological conditions and dengue fever incidence in coastal southeastern China from 2013 to 2019. JAMA Netw Open 2023; 6(1): e2249440.

[37] Wu X, Lang L, Ma W, Song T, Kang M, He J, et al. Non-linear effects of mean temperature and relative humidity on dengue incidence in Guangzhou, China. Sci Total Environ 2018;628-629:766-771.

[38] Marcantonio M, Reyes T, Barker CM. Quantifying Aedes aegypti dispersal in space and time: A modeling approach. Ecosphere 2019; 10(12): e02977. doi: 10.1002/ecs2.2977.

[39] Bartlett-Healy K, Healy SP, Hamilton GC. A model to predict Evaporation rates in habitats used by container-dwelling mosquitoes. J Med Entomol 2011; 48(3): 712-716.

[40] Djaafara BA, Elyazar IRF, Silalahi FSM, Surya A, Handito A, Thohir B, et al. Dengue transmission heterogeneity across Indonesia’s archipelago: Climate-driven spatiotemporal patterns and policy implications. PLoS Negl Trop Dis 2026; 20(3): e0014135.